Air pollution is a 3-dimensional (3D) dynamic process in the global atmospheric system. Considerable air pollution data exists today, collected by many Earth-Observing agencies, but, the ability to derive actionable information from it is limited due to multiple factors including: (i) fragmented, patchy terrestrial networks and limited space-based monitoring which see mostly narrow slices of atmosphere, (ii) data flow from point of observation to end-user typically does not happen on a real-time or near real-time basis, (iii) data is captured in a “snapshot” format, or a mostly two dimensional (2D) approach which constrains the ability to understand air pollution movement and such desirable aspects as tracing air pollution back to its source(s).
Several other factors inhibit the ability to create actionable air pollution information including the fact that most existing data is segmented, hard to access, typically formatted for science research and broad data integration has not been achieved. Specifically, integration of historical data with real time data is not yet generally available; integration of such information with predictive analytical models is not yet generally available, and; integration of air pollution data with corresponding wind data is not yet generally available. In addition, only pedestrian approaches to visualizing the data are being taken which greatly limits insight generation. Contrast the latter with taking a strategic visualization approach which tailors information presentation to the specific needs of the audience/customer and captivates.
The U.S. Environmental Protection Agency (EPA), the European Environment Agency (EEA), and the Chinese government are among the main drivers behind existing terrestrial networks. Outside of the U.S. and Europe, very little reliable terrestrial network monitoring exists. The terrestrial-based air quality surveillance system in the U.S. consists of a network of monitoring stations designated as “SLAMS”, “NAMS”, and “PAMS”. SLAMS, or State and Local Air Monitoring Stations, measure ambient concentrations of pollutants (for which standards have been established). NAMS, or National Air Monitoring Stations, are a subset of SLAMS, and are urban area long-term air monitoring networks that provide a systematic, consistent database for air quality comparisons and trends analysis. These sites must meet more stringent siting, equipment type, and quality assurance criteria. PAMS, or Photochemical Monitoring Stations, are also a subset of SLAMS, and monitor volatile organic compounds as ozone precursors during the summer ozone season.
A range of satellites exist today with air pollution data gathering capabilities; including, for example, Terra, Aqua, Aura, MetOp and GOES. The payloads measure certain aspects of air pollution over certain areas of the U.S. and Europe. With their mostly low-earth-orbit designs, data monitoring occurs more on a daily basis versus hourly. Data feeds from these satellites are not proactively fused with respective in-country terrestrial networks. It is therefore a critical need to efficiently ensure the collection of near-real-time or real-time data, together with data gathering from other governmental and commercial atmospheric data sources which aggregate such sources of information. Given the above, a comprehensive approach that integrates, synthesizes and innovates is required to appropriately address the air pollution information opportunity. This is a cost-driven opportunity comprised of hundreds of billions of dollars lost annually in such areas as health care and agriculture.